Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. METHODS: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. RESULTS: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. CONCLUSIONS: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Long-Term Outcomes Associated With ß-Lactam Allergies.

Importance: ß-lactam (BL) allergies are the most common drug allergy worldwide, but most are reported in error. BL allergies are also well-established risk factors for adverse drug events and antibiotic-resistant infections during inpatient health care encounters, but the understanding of the long-term outcomes of patients with BL allergies remains limited. Objective: To evaluate the long-term clinical outcomes of patients with BL allergies. Design, Setting, and Participants: This longitudinal retrospective cohort study was conducted at a single regional health care system in western Pennsylvania. Electronic health records were analyzed for patients who had an index encounter with a diagnosis of sepsis, pneumonia, or urinary tract infection between 2007 and 2008. Patients were followed-up until death or the end of 2018. Data analysis was performed from January 2022 to January 2024. Exposure: The presence of any BL class antibiotic in the allergy section of a patient's electronic health record, evaluated at the earliest occurring observed health care encounter. Main Outcomes and Measures: The primary outcome was all-cause mortality, derived from the Social Security Death Index. Secondary outcomes were defined using laboratory and microbiology results and included infection with methicillin-resistant Staphylococcus aureus (MRSA), Clostridium difficile, or vancomycin-resistant Enterococcus (VRE) and severity and occurrence of acute kidney injury (AKI). Generalized estimating equations with a patient-level panel variable and time exposure offset were used to evaluate the odds of occurrence of each outcome between allergy groups. Results: A total of 20 092 patients (mean [SD] age, 62.9 [19.7] years; 12 231 female [60.9%]), of whom 4211 (21.0%) had BL documented allergy and 15 881 (79.0%) did not, met the inclusion criteria. A total of 3513 patients (17.5%) were Black, 15 358 (76.4%) were White, and 1221 (6.0%) were another race. Using generalized estimating equations, documented BL allergies were not significantly associated with the odds of mortality (odds ratio [OR], 1.02; 95% CI, 0.96-1.09). BL allergies were associated with increased odds of MRSA infection (OR, 1.44; 95% CI, 1.36-1.53), VRE infection (OR, 1.18; 95% CI, 1.05-1.32), and the pooled rate of the 3 evaluated antibiotic-resistant infections (OR, 1.33; 95% CI, 1.30-1.36) but were not associated with C difficile infection (OR, 1.04; 95% CI, 0.94-1.16), stage 2 and 3 AKI (OR, 1.02; 95% CI, 0.96-1.10), or stage 3 AKI (OR, 1.06; 95% CI, 0.98-1.14). Conclusions and Relevance: Documented BL allergies were not associated with the long-term odds of mortality but were associated with antibiotic-resistant infections. Health systems should emphasize accurate allergy documentation and reduce unnecessary BL avoidance.

Pharmacist-implemented intervention to surmount COVID-19 vaccination hesitancy in adults with substance use disorders.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: Substance use disorders (SUDs) increase the risk and severity of infectious diseases, including coronavirus disease 2019 (COVID-19). Adults with a co-occurring SUD and psychiatric disorder were studied to elucidate the association between SUD severity and (1) COVID-19 vaccination status, (2) receptivity to a one-session intervention with a pharmacist advocating the benefits of vaccination, and (3) acceptance of referral for vaccination following the intervention. METHODS: COVID-19 vaccination status was recorded in 460 adults with SUD (324 males and 136 females) upon entry into inpatient treatment. A 2-parameter item response theory (IRT) model quantified SUD severity. Pharmacist-delivered intervention, modeled after the screening, brief intervention, and referral to treatment (SBIRT) protocol, was offered to unvaccinated participants. RESULTS: Higher SUD severity was associated with a lower vaccination rate. Nicotine, opioid, and sedative use disorders were most frequently associated with unvaccinated status. SUD severity was not associated with receptivity to intervention advocating vaccination or subsequent acceptance of a referral for vaccination. The portion of the sample that received the intervention was over 7 times more likely to accept a referral for vaccination when compared to participants who rejected the intervention (20.8% vs 2.8%). CONCLUSION: Pharmacist-administered intervention produced motivation for vaccination in a number of recipients; however, receptivity to the intervention was not related to SUD severity.

Enhancing post-traumatic stress disorder patient assessment: Leveraging Natural Language Processing for Research of Domain Criteria Identification using electronic medical records.

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. Methods: In our study, we created an NLP workflow to analyze electronic medical record (EMR) data, and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, allmpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. Results: The sentence transformer model demonstrated superior F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health.

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.

Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models.

BACKGROUND: Identifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use DeepBiomarker2, an updated version of our deep learning model to predict the adverse events among patients with comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD), a high-risk population. METHODS: We analyzed electronic medical records of 5565 patients from University of Pittsburgh Medical Center to predict adverse events (opioid use disorder, suicide related events, depression, and death) within 3 months at any encounter after the diagnosis of PTSD+AUD by using DeepBiomarker2. We integrated multimodal information including: lab tests, medications, co-morbidities, individual and neighborhood level social determinants of health (SDoH), psychotherapy and veteran data. RESULTS: DeepBiomarker2 achieved an area under the receiver operator curve (AUROC) of 0.94 on the prediction of adverse events among those PTSD+AUD patients. Medications such as vilazodone, dronabinol, tenofovir, suvorexant, modafinil, and lamivudine showed potential for risk reduction. SDoH parameters such as cognitive behavioral therapy and trauma focused psychotherapy lowered risk while active veteran status, income segregation, limited access to parks and greenery, low Gini index, limited English-speaking capacity, and younger patients increased risk. CONCLUSIONS: Our improved version of DeepBiomarker2 demonstrated its capability of predicting multiple adverse event risk with high accuracy and identifying potential risk and beneficial factors.

Prediction of Adverse Events Risk in Patients with Comorbid Post- Traumatic Stress Disorder and Alcohol Use Disorder Using Electronic Medical Records by Deep Learning Models.

Background: Prediction of high-risk events in mental disorder patients is crucial. In our previous study, we developed a deep learning model: DeepBiomarker by using electronic medical records (EMR) to predict suicide related event (SRE) risk in post-traumatic stress disorder (PTSD) patients. Methods: We applied DeepBiomarker2 through data integration of multimodal information: lab test, medication, co-morbidities, and social determinants of health. We analyzed EMRs of 5,565 patients from University of Pittsburgh Medical Center with a diagnosis of PTSD and alcohol use disorder (AUD) on risk of developing an adverse event (opioid use disorder, SREs, depression and death). Results: DeepBiomarker2 predicted whether a PTSD + AUD patient will have a diagnosis of any adverse events (SREs, opioid use disorder, depression, death) within 3 months with area under the receiver operator curve (AUROC) of 0.94. We found piroxicam, vilazodone, dronabinol, tenofovir, suvorexant, empagliflozin, famciclovir, veramyst, amantadine, sulfasalazine, and lamivudine to have potential to reduce risk. Conclusions: DeepBiomarker2 can predict multiple adverse event risk with high accuracy and identify potential risk and beneficial factors. Our results offer suggestions for personalized interventions in a variety of clinical and diverse populations.

Substance use disorder is the outcome of deviant socialization: A prospective investigation spanning childhood to adulthood.

OBJECTIVE: Disinhibitory behavior during childhood and adolescence has been frequently shown to amplify the risk for substance use disorder (SUD) in adulthood. This prospective study examined the hypothesis that poor communication with parents and association with deviant peers comprise an SUD-promoting environtype which catalyzes transition of disinhibitory behavior toward SUD. METHOD: Male (N = 499) and female (N = 195) youths were tracked from 10 to 12 to 30 years of age. Path analysis evaluated the patterning of disinhibitory behavior and social environment during childhood on substance use during adolescence, and antisocial personality without co-occurring SUD in early adulthood and subsequently substance use disorder (SUD). RESULTS: Disinhibitory behavior (SUD vulnerability) in childhood predicts antisociality without SUD (age 22) that segues to SUD (age 23-30) whereas the environtype (parents and peers) predicts substance use during adolescence which predicts antisocial personality leading to SUD. Antisociality without SUD in early adulthood mediates the association of substance use during adolescence and SUD. CONCLUSION: Disinhibitory behavior and deviance-promoting social environment conjointly promote development of SUD via deviant socialization.

DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health.

Introduction: Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. In our previous study, we developed a deep learning-based model, DeepBiomarker by utilizing electronic medical records (EMR) to predict the outcomes of patients with suicide-related events in post-traumatic stress disorder (PTSD) patients. Methods: We improved our deep learning model to develop DeepBiomarker2 through data integration of multimodal information: lab tests, medication use, diagnosis, and social determinants of health (SDoH) parameters (both individual and neighborhood level) from EMR data for outcome prediction. We further refined our contribution analysis for identifying key factors. We applied DeepBiomarker2 to analyze EMR data of 38,807 patients from University of Pittsburgh Medical Center diagnosed with PTSD to determine their risk of developing alcohol and substance use disorder (ASUD). Results: DeepBiomarker2 predicted whether a PTSD patient will have a diagnosis of ASUD within the following 3 months with a c-statistic (receiver operating characteristic AUC) of 0·93. We used contribution analysis technology to identify key lab tests, medication use and diagnosis for ASUD prediction. These identified factors imply that the regulation of the energy metabolism, blood circulation, inflammation, and microbiome is involved in shaping the pathophysiological pathways promoting ASUD risks in PTSD patients. Our study found protective medications such as oxybutynin, magnesium oxide, clindamycin, cetirizine, montelukast and venlafaxine all have a potential to reduce risk of ASUDs. Discussion: DeepBiomarker2 can predict ASUD risk with high accuracy and can further identify potential risk factors along with medications with beneficial effects. We believe that our approach will help in personalized interventions of PTSD for a variety of clinical scenarios.

The Concept of Resistance to Substance Use and a Research Approach: The Resist! Project.

Illicit substance use is dangerous in both acute and chronic forms, frequently resulting in lethal poisoning, addiction, and other negative consequences. Similar to research in other psychiatric conditions, whose ultimate goal is to enable effective prevention and treatment, studies in substance use are focused on factors elevating the risk for the disorder. The rapid growth of the substance use problem despite the effort invested in fighting it, however, suggests the need in changing the research approach. Instead of attempting to identify risk factors, whose neutralization is often infeasible if not impossible, it may be more promising to systematically reverse the perspective to the factors enhancing the aspect of liability to disorder that shares the same dimension but is opposite to risk, that is, resistance to substance use. Resistance factors, which enable the majority of the population to remain unaffected despite the ubiquity of psychoactive substances, may be more amenable to translation. While the resistance aspect of liability is symmetric to risk, the resistance approach requires substantial changes in sampling (high-resistance rather than high-risk) and using quantitative indices of liability. This article provides an overview and a practical approach to research in resistance to substance use/addiction, currently implemented in a NIH-funded project. The project benefits from unique opportunities afforded by the data originating from two longitudinal twin studies, the Virginia Twin Study of Adolescent and Behavioral Development and the Minnesota Twin Family Study. The methodology described is also applicable to other psychiatric disorders.

Physical inactivity during adolescence heightens risk for cannabis use disorder in adulthood.

The association between physical inactivity and substance use throughout adolescence was prospectively investigated in relation to developing cannabis use disorder (CUD). Physical inactivity and substance use in males (N = 462) and females (N = 178) were measured at 12-14, 16, 19, and 22 years of age in a repeated measures design. A structured diagnostic interview was administered to formulate current CUD diagnosis at 22 years of age. Mixture modeling path analysis evaluated the association between physical inactivity, substance use, and CUD. Males: Slope of physical inactivity increase spanning 12-22 years of age mediates the association between number of parents with substance use disorder (SUD) and rate of increase in substance use frequency (prodrome) which mediates the association between physical inactivity (hypothesized vulnerability) and CUD. Females: Number of SUD parents predicts slope of physical inactivity increase in daughters throughout adolescence which covaries with slope of increasing substance use frequency culminating in CUD. The association between parental SUD load (number of SUD affected parents) and CUD was found to not be mediated by physical inactivity. Rate of increase in physical inactivity during adolescence in males and females is a facet of the vulnerability for CUD. These results have ramifications for prevention considering that numerous cognitive, behavior, and emotion features of CUD vulnerability are attenuated by exercise. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Impact of pharmacist motivational interviewing on hepatitis B vaccination in adults with diabetes.

BACKGROUND: In 2011, the Advisory Committee on Immunization Practices recommended hepatitis B (HepB) vaccination for previously unvaccinated adults (aged 19-59 years) with diabetes. Despite these recommendations, vaccination coverage for HepB vaccination for persons with diabetes remains low. OBJECTIVES: The primary objective was to determine the impact of a community pharmacist-led motivational interviewing (MI) intervention on HepB vaccination initiation among adults with diabetes who were previously unvaccinated against HepB. The secondary objective was to describe HepB vaccination series completion among adults with diabetes who initiated the first dose of a HepB vaccine. METHODS: A prospective, nonrandomized, controlled cluster trial was conducted across 58 regional grocery store chain pharmacies: a total of 29 pharmacies in the MI group and 29 pharmacies in the control group. Pharmacy location-level baseline data were collected during a 12-month pre-program period. The MI program was delivered over 10 months. Alerts were generated during prescription processing throughout the study period for eligible patients at each MI pharmacy location. The MI consisted of a face-to-face conversation between the pharmacist and the patient at the time of prescription pick-up. The difference in the primary outcome of HepB vaccination series initiation between patients receiving MI and control patients was assessed using a difference-in-differences analysis. For series completion, patients who initiated the HepB vaccination series were followed up for over 12 months after their first HepB vaccine dose. RESULTS: There was a statistically significant 3.711% increase in HepB vaccination when comparing eligible individuals who received the MI intervention (n = 1569) to eligible individuals in the control group (n = 3640). Of the patients in the MI group who initiated HepB vaccination, 40 of 65 patients (61.5%) completed the vaccination series. CONCLUSION: A pharmacist-led MI intervention increased HepB vaccination rates among adult patients with diabetes. Community pharmacists can effectively provide vaccinations that require multiple doses to complete the vaccination series.

Bidirectional associations between self-regulation and deviance from adolescence to adulthood.

Self-regulation is considered a major predictor of crime and deviant behavior. However, longitudinal research investigating these associations, frequently looked only at the effect of self-regulation on deviant behavior, but not the other way around. The current study argued that deviance may contribute to later problems in self-regulation, and examined bidirectional associations, comparing a unidirectional and bidirectional model of associations between these variables. A Random Intercept Cross-Lagged Panel Model and eight data waves from 772 participants, aged 10-12 years to 30 years were used. Results showed that a bidirectional model fit the data better than a unidirectional model. The final model revealed an influence of deviance on self-regulation mainly in adolescence, whereas self-regulation influenced deviance only over two time points in adulthood. The results suggest that, in adolescence, problems in self-regulation may follow, rather than precede deviant behavior. Thus, decreasing deviant behavior or intervening in the aftermaths of deviant behavior in adolescence might have a positive effect on self-regulation in young adulthood, lowering the chance of adult deviant behavior. The current study shows that the long-presumed directionality of self-regulation to deviance can lead to bias, and more rigorous longitudinal research is needed in order to further inform theory and practice.

Elaborating on the longitudinal measurement invariance and construct validity of the triarchic psychopathy scales from the Multidimensional Personality Questionnaire.

Because the construct of psychopathy is of chief interest across different disciplines, spanning developmental, clinical, and forensic psychology, its assessment bears far-reaching implications. One prominent contemporary conceptualization of psychopathy, the Triarchic Model, posits that a psychopathic personality encompasses three phenotypic constructs: boldness, meanness, and disinhibition. Recently, triarchic scales have been derived based on items from the Multidimensional Personality Questionnaire (MPQ), and the psychometric characteristics of this approach (MPQ-triarchic [MPQ-Tri]) are promising. The present study examined the longitudinal measurement invariance and the construct validity of the MPQ-Tri scales in a large and diverse high-risk sample (N = 716) across four time points from age 16-25. First, we report and discuss implications of confirmatory and exploratory factor analyses of the MPQ-Tri scales. Next, we report evidence for longitudinal configural and partial scalar invariance. In addition, in line with previous studies, MPQ-Boldness showed relatively higher levels of rank-order and mean-level stability compared to MPQ-Meanness and Disinhibition. Finally, in terms of construct validity, the MPQ-Tri scales showed a pattern of association with external correlates across internalizing and externalizing domains that were largely in line with theoretical expectations. One partial exception concerned the limited discriminant validity of the MPQ-Meanness and Disinhibition scales. On balance, the present findings suggest that the MPQ-Tri scales fulfill their intended purpose, with some noted limitation, and provide grounds for the use of the MPQ-Tri scales in developmentally-informed studies on the etiology and consequences of psychopathy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

An Emulation of Randomized Trials of Administrating Antipsychotics in PTSD Patients for Outcomes of Suicide-Related Events.

Post-traumatic stress disorder (PTSD) is a prevalent mental disorder marked by psychological and behavioral changes. Currently, there is no consensus of preferred antipsychotics to be used for the treatment of PTSD. We aim to discover whether certain antipsychotics have decreased suicide risk in the PTSD population, as these patients may be at higher risk. A total of 38,807 patients were identified with a diagnosis of PTSD through the ICD9 or ICD10 codes from January 2004 to October 2019. An emulation of randomized clinical trials was conducted to compare the outcomes of suicide-related events (SREs) among PTSD patients who ever used one of eight individual antipsychotics after the diagnosis of PTSD. Exclusion criteria included patients with a history of SREs and a previous history of antipsychotic use within one year before enrollment. Eligible individuals were assigned to a treatment group according to the antipsychotic initiated and followed until stopping current treatment, switching to another same class of drugs, death, or loss to follow up. The primary outcome was to identify the frequency of SREs associated with each antipsychotic. SREs were defined as ideation, attempts, and death by suicide. Pooled logistic regression methods with the Firth option were conducted to compare two drugs for their outcomes using SAS version 9.4 (SAS Institute, Cary, NC, USA). The results were adjusted for baseline characteristics and post-baseline, time-varying confounders. A total of 5294 patients were eligible for enrollment with an average follow up of 7.86 months. A total of 157 SREs were recorded throughout this study. Lurasidone showed a statistically significant decrease in SREs when compared head to head to almost all the other antipsychotics: aripiprazole, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (p < 0.0001 and false discovery rate-adjusted p value < 0.0004). In addition, olanzapine was associated with higher SREs than quetiapine and risperidone, and ziprasidone was associated with higher SREs than risperidone. The results of this study suggest that certain antipsychotics may put individuals within the PTSD population at an increased risk of SREs, and that careful consideration may need to be taken when prescribed.

Developmental trajectory classes in psychological dysregulation predict later decision-making competence.

Adolescence and emerging adulthood are developmental periods associated with increased risk taking, including alcohol and substance use and antisocial behaviors. Typical psychological growth from adolescence into early adulthood reflects increases in traits related to psychological regulation (e.g., greater emotional stability and less impulsivity), which are typically considered protective factors against risk behaviors. However, individuals may vary greatly in their development of these characteristics. This study examines the degree to which heterogeneity in developmental trajectories of psychological regulation are associated with later performance on decision-making skills battery. In this study, psychological regulation was assessed at age 10-12, with follow-up assessments at 14, 16, and 19 years. At age 19, we administered the Youth Decision-Making Competence (DMC; Parker & Fischhoff, 2005) measure. Correlational analyses revealed that lower psychological regulation, as early as age 10, was associated with lower DMC scores. A latent class growth mixture model yielded three distinct developmental trajectory classes of psychological dysregulation: (a) a Moderate-Stable group, a modal class that demonstrated stable and average regulative tendencies throughout adolescence, (b) a Low-Decreasing group, which demonstrated greater self-regulation throughout childhood, and a (c) High-Increasing group, which demonstrated low self-regulative tendencies (higher dysregulation) at age 10 that became increasingly dysregulated throughout adolescence. Individuals in the High-Increasing group demonstrated lower DMC performance than those in the Moderate-Stable and Low-Decreasing groups. Our findings also reinforce past work that indicates considerable individual differences in intra-individual change across adolescence, and that early patterns of psychological dysregulation development can impact later decision-making tendencies.

Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder.

Around 800,000 people worldwide die from suicide every year and it's the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

Forecasting Opioid Use Disorder at 25 Years of Age in 16-Year-Old Adolescents.

OBJECTIVE: To evaluate the accuracy of detecting 16-year-old male (n = 465) and female (n = 162) youths who subsequently manifest opioid use disorder (OUD) at 25 years of age. We hypothesized that the combined measures of 2 components of etiology, heritable risk, and substance use, accurately detect youths who develop OUD. STUDY DESIGN: Heritable risk was measured by the transmissible liability index (TLI). Severity of the prodrome presaging OUD was quantified by the revised Drug Use Screening Inventory containing the consumption frequency index (CFI) documenting substance use events during the past month and the overall problem density (OPD) score indicating co-occurring biopsychosocial problems. Diagnosis of OUD was formulated by a clinical committee based on results of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition in conjunction with medical and social history records. RESULTS: Bivariate analysis shows that the TLI, CFI, and OPD scores at 16 years of age predict OUD at 25 years. Multivariate modeling indicates that the TLI combined with the CFI predict OUD with 86% accuracy (sensitivity = 87%; specificity = 62%). The TLI and CFI at 16 years of age mediate the association between parental substance use disorder and OUD in offspring at 25 years of age, indicating that these measures respectively evaluate risk and prodrome. CONCLUSIONS: These results demonstrate the feasibility of identifying youths requiring intervention to prevent OUD.

The Performance of Gene Expression Signature-Guided Drug-Disease Association in Different Categories of Drugs and Diseases.

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs-disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug-disease associations through the analysis of the similarity of GES profiles on known pairs of drug-disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug-disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.

Patient-Reported Outcomes Associated With Sedation and Agitation Intensity in the Critically Ill.

BACKGROUND: Patient-reported outcomes are essential to understand the relationship between patients' perception of sedation and clinicians' assessments of sedation. OBJECTIVES: To evaluate the association between sedation and agitation indexes and patient-reported outcomes of sedation and analgesia. METHODS: This prospective, single-center, observational study included adult patients who were continuously sedated for at least 24 hours in a medical or surgical/ trauma intensive care unit. Patients were interviewed after sedation was discontinued regarding their satisfaction with the quality of sedation and potentially related factors. The primary outcome was the correlation between sedation and agitation indexes and patient-reported outcomes. RESULTS: A total of 68 patients were interviewed after sedation. Of these, 29 (42.6%) described their overall feelings about their experience while receiving mechanical ventilation in the intensive care unit as "pleasant". When asked about their desires if they were to experience the situation again, 29 patients (42.6%) reported that they would want more sedation. Agitation index was statistically significantly correlated with several patient-reported outcomes. Receiving mechanical ventilation (r = 0.41, P = .002), the amount of noise (r = 0.34, P = .01), suctioning (r = 0.32, P = .02), difficulty resting or sleeping (r = 0.39, P = .003), inability to communicate by talking (r = 0.36, P = .008), anxiety (r = 0.29, P = .03), panic (r = 0.3, P = .02), and frustration (r = 0.47, P < .001) were associated with a higher agitation index. CONCLUSION: Agitation index was significantly associated with several patient-reported outcomes and thus seems to be a promising descriptor of patients' experience.